Multi-eye Visual Fusion Encoderless Control with Permanent Magnet Synchronous Machines

Motion capture (MoCap) technology can be implemented using computer vision (CV)-based target position sensing methods. In modern industrial applications, CV-based position measurement techniques are increasingly emerging as a promising alternative to traditional encoders in servo drives, offering the potential to reduce system costs while maintaining performance. Although MoCap technologies have made significant progress in the past decades, CV-based systems still face challenges related to limited measurement accuracy and delayed real-time responsiveness, especially in cost-sensitive applications where both precise position recognition and real-time response are critical. To resolve these limitations, this paper presents a visual-electromechanical (EM) sensing fusion control framework. A Color Visual Wavelet-Transformer (CVWT) network is designed that utilizes color features as input, effectively preserving critical information while reducing training complexity and computational cost. The CVWT network integrates a wavelet transform module with a Transformer module to perform multi-scale and multi-level feature extraction and modeling on visual data acquired from dual cameras. In addition, electrical and mechanical models are incorporated into the state estimation framework, and an Extended Kalman Filter (EKF) is employed to fuse multi-source perceptual data. Experimental results demonstrate that under a maximum rotational speed of 25 r/min, the system achieves a position control accuracy of up to 0.47deg, validating the effectiveness and feasibility of the proposed method within a low-cost vision-based framework.